Import model objects
load('/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Data/GER/ger_list_results_fixed_window.RData')
load('/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Data/US/us_list_results_fixed_window.RData')
library(lmtest)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(car)
## Loading required package: carData
library(survival)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.0 ✓ purrr 0.3.3
## ✓ tibble 3.0.0 ✓ dplyr 0.8.5
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## Warning: package 'tibble' was built under R version 3.6.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x dplyr::recode() masks car::recode()
## x purrr::some() masks car::some()
ger_list_results$ger_lm_prev_slope$pers_o$lm_all %>% resid() %>% ks.test(y=pnorm)
##
## One-sample Kolmogorov-Smirnov test
##
## data: .
## D = 0.12717, p-value = 6.229e-06
## alternative hypothesis: two-sided
us_list_results$us_lm_prev_slope$pers_o$lm_all %>% resid() %>% ks.test(y=pnorm)
##
## One-sample Kolmogorov-Smirnov test
##
## data: .
## D = 0.15739, p-value < 2.2e-16
## alternative hypothesis: two-sided
ger_list_results$ger_lm_prev_slope$pers_o$lm_base %>% bptest()
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.20014, df = 1, p-value = 0.6546
Define Functions
list_iterater <- function(models, test) {
for(i in models){
for(j in i){
if(test == 'qq'){j %>% plot(2)}
if(test == 'ks'){j %>% resid() %>% ks.test(y=pnorm) %>% print()}
if(test == 'bp'){j %>% bptest() %>% print()}
if(test == 'ph'){j %>% cox.zph() %>% print()}
}
}
}
Assumptions GER COVID-19 onsets
list_iterater(ger_list_results$ger_cox_prev_onset, test = 'ph')
## chisq df p
## pers 6.21 1 0.013
## GLOBAL 6.21 1 0.013
## chisq df p
## pers 4.007 1 0.045
## age 21.844 1 3.0e-06
## male 0.454 1 0.500
## conservative 15.769 1 7.2e-05
## GLOBAL 25.231 4 4.5e-05
## chisq df p
## pers 1.30051 1 0.25
## academics 0.10832 1 0.74
## medinc 0.00668 1 0.93
## manufact 0.07463 1 0.78
## GLOBAL 1.38751 4 0.85
## chisq df p
## pers 6.89 1 0.0087
## airport_dist 7.94 1 0.0048
## tourism 1.65 1 0.1994
## healthcare 6.31 1 0.0120
## popdens 6.31 1 0.0120
## GLOBAL 17.20 5 0.0041
## chisq df p
## pers 1.05049 1 0.30539
## age 11.81505 1 0.00059
## male 4.76514 1 0.02904
## conservative 4.75085 1 0.02928
## academics 0.11884 1 0.73030
## medinc 2.27744 1 0.13127
## manufact 0.49479 1 0.48180
## airport_dist 0.69603 1 0.40412
## tourism 0.07452 1 0.78486
## healthcare 0.00138 1 0.97032
## popdens 1.36622 1 0.24246
## GLOBAL 17.21722 11 0.10161
## chisq df p
## pers 2.51 1 0.11
## GLOBAL 2.51 1 0.11
## chisq df p
## pers 7.888 1 0.00498
## age 19.642 1 9.3e-06
## male 0.658 1 0.41721
## conservative 14.163 1 0.00017
## GLOBAL 23.934 4 8.2e-05
## chisq df p
## pers 1.6479 1 0.20
## academics 0.0923 1 0.76
## medinc 0.0737 1 0.79
## manufact 0.0601 1 0.81
## GLOBAL 1.9272 4 0.75
## chisq df p
## pers 3.23 1 0.0723
## airport_dist 8.82 1 0.0030
## tourism 1.55 1 0.2126
## healthcare 6.01 1 0.0142
## popdens 6.41 1 0.0114
## GLOBAL 16.26 5 0.0061
## chisq df p
## pers 3.45e+00 1 0.06333
## age 1.28e+01 1 0.00034
## male 4.89e+00 1 0.02701
## conservative 5.00e+00 1 0.02530
## academics 8.68e-02 1 0.76827
## medinc 2.30e+00 1 0.12979
## manufact 6.41e-01 1 0.42342
## airport_dist 8.22e-01 1 0.36449
## tourism 6.81e-02 1 0.79418
## healthcare 9.25e-04 1 0.97574
## popdens 1.38e+00 1 0.23943
## GLOBAL 1.87e+01 11 0.06668
## chisq df p
## pers 10.2 1 0.0014
## GLOBAL 10.2 1 0.0014
## chisq df p
## pers 9.075 1 0.0026
## age 20.874 1 4.9e-06
## male 0.256 1 0.6128
## conservative 15.413 1 8.6e-05
## GLOBAL 29.120 4 7.4e-06
## chisq df p
## pers 3.3334 1 0.068
## academics 0.3435 1 0.558
## medinc 0.0519 1 0.820
## manufact 0.0950 1 0.758
## GLOBAL 3.3556 4 0.500
## chisq df p
## pers 8.30 1 0.0040
## airport_dist 7.50 1 0.0062
## tourism 1.70 1 0.1924
## healthcare 7.55 1 0.0060
## popdens 6.36 1 0.0117
## GLOBAL 21.03 5 0.0008
## chisq df p
## pers 4.09e+00 1 0.04323
## age 1.17e+01 1 0.00064
## male 4.38e+00 1 0.03633
## conservative 4.86e+00 1 0.02754
## academics 2.09e-01 1 0.64747
## medinc 2.49e+00 1 0.11445
## manufact 4.16e-01 1 0.51913
## airport_dist 7.69e-01 1 0.38049
## tourism 8.12e-02 1 0.77565
## healthcare 5.23e-04 1 0.98176
## popdens 1.31e+00 1 0.25286
## GLOBAL 1.83e+01 11 0.07556
## chisq df p
## pers 7.14 1 0.0076
## GLOBAL 7.14 1 0.0076
## chisq df p
## pers 3.46 1 0.0628
## age 21.13 1 4.3e-06
## male 0.44 1 0.5072
## conservative 15.05 1 0.0001
## GLOBAL 28.94 4 8.1e-06
## chisq df p
## pers 2.9859 1 0.084
## academics 0.0492 1 0.825
## medinc 0.0130 1 0.909
## manufact 0.0662 1 0.797
## GLOBAL 3.1061 4 0.540
## chisq df p
## pers 7.98 1 0.00474
## airport_dist 8.47 1 0.00362
## tourism 1.55 1 0.21352
## healthcare 5.87 1 0.01537
## popdens 6.24 1 0.01247
## GLOBAL 20.74 5 0.00091
## chisq df p
## pers 2.34952 1 0.1253
## age 12.10476 1 0.0005
## male 5.08073 1 0.0242
## conservative 4.84600 1 0.0277
## academics 0.03360 1 0.8546
## medinc 2.16525 1 0.1412
## manufact 0.63890 1 0.4241
## airport_dist 0.64944 1 0.4203
## tourism 0.06193 1 0.8035
## healthcare 0.00924 1 0.9234
## popdens 1.17671 1 0.2780
## GLOBAL 21.93744 11 0.0249
## chisq df p
## pers 5.91 1 0.015
## GLOBAL 5.91 1 0.015
## chisq df p
## pers 4.570 1 0.033
## age 19.880 1 8.2e-06
## male 0.332 1 0.564
## conservative 15.516 1 8.2e-05
## GLOBAL 26.962 4 2.0e-05
## chisq df p
## pers 0.507 1 0.48
## academics 0.202 1 0.65
## medinc 0.128 1 0.72
## manufact 0.280 1 0.60
## GLOBAL 1.003 4 0.91
## chisq df p
## pers 5.90 1 0.01512
## airport_dist 7.85 1 0.00509
## tourism 1.40 1 0.23743
## healthcare 5.39 1 0.02023
## popdens 7.23 1 0.00716
## GLOBAL 20.61 5 0.00096
## chisq df p
## pers 0.7484 1 0.38699
## age 10.8876 1 0.00097
## male 4.0270 1 0.04478
## conservative 5.2602 1 0.02182
## academics 0.2251 1 0.63517
## medinc 1.1519 1 0.28314
## manufact 0.1375 1 0.71080
## airport_dist 0.8884 1 0.34590
## tourism 0.0349 1 0.85181
## healthcare 0.0493 1 0.82426
## popdens 1.4904 1 0.22216
## GLOBAL 15.5926 11 0.15694
Assumptions US COVID-19 onsets
list_iterater(us_list_results$us_cox_prev_onset, test = 'ph')
## chisq df p
## pers 133 1 <2e-16
## GLOBAL 133 1 <2e-16
## chisq df p
## pers 106.580 1 < 2e-16
## age 0.453 1 0.5
## male 21.391 1 3.7e-06
## conservative 102.647 1 < 2e-16
## GLOBAL 172.622 4 < 2e-16
## chisq df p
## pers 96.8 1 < 2e-16
## academics 144.2 1 < 2e-16
## medinc 47.3 1 5.9e-12
## manufact 61.3 1 5.0e-15
## GLOBAL 182.7 4 < 2e-16
## chisq df p
## pers 92.07 1 < 2e-16
## airport_dist 8.83 1 0.003
## tourism 15.46 1 8.4e-05
## healthcare 37.50 1 9.1e-10
## popdens 3.87 1 0.049
## GLOBAL 122.81 5 < 2e-16
## chisq df p
## pers 71.8062 1 < 2e-16
## age 0.0164 1 0.89806
## male 21.4651 1 3.6e-06
## conservative 70.0537 1 < 2e-16
## academics 110.6622 1 < 2e-16
## medinc 32.3876 1 1.3e-08
## manufact 46.1464 1 1.1e-11
## airport_dist 2.0257 1 0.15466
## tourism 13.2975 1 0.00027
## healthcare 25.6451 1 4.1e-07
## popdens 32.9347 1 9.5e-09
## GLOBAL 173.6175 11 < 2e-16
## chisq df p
## pers 0.729 1 0.39
## GLOBAL 0.729 1 0.39
## chisq df p
## pers 2.56 1 0.11
## age 1.30 1 0.25
## male 20.65 1 5.5e-06
## conservative 107.90 1 < 2e-16
## GLOBAL 124.50 4 < 2e-16
## chisq df p
## pers 0.112 1 0.74
## academics 153.781 1 < 2e-16
## medinc 53.480 1 2.6e-13
## manufact 64.290 1 1.1e-15
## GLOBAL 170.151 4 < 2e-16
## chisq df p
## pers 0.425 1 0.5143
## airport_dist 7.913 1 0.0049
## tourism 15.657 1 7.6e-05
## healthcare 41.027 1 1.5e-10
## popdens 1.702 1 0.1921
## GLOBAL 59.516 5 1.5e-11
## chisq df p
## pers 2.14e-01 1 0.64358
## age 4.78e-03 1 0.94491
## male 1.97e+01 1 9.2e-06
## conservative 7.15e+01 1 < 2e-16
## academics 1.17e+02 1 < 2e-16
## medinc 3.90e+01 1 4.2e-10
## manufact 4.88e+01 1 2.8e-12
## airport_dist 1.27e+00 1 0.26002
## tourism 1.39e+01 1 0.00019
## healthcare 2.87e+01 1 8.3e-08
## popdens 3.04e+01 1 3.5e-08
## GLOBAL 1.71e+02 11 < 2e-16
## chisq df p
## pers 1.91 1 0.17
## GLOBAL 1.91 1 0.17
## chisq df p
## pers 0.913 1 0.34
## age 1.251 1 0.26
## male 22.838 1 1.8e-06
## conservative 111.394 1 < 2e-16
## GLOBAL 124.947 4 < 2e-16
## chisq df p
## pers 0.964 1 0.33
## academics 148.095 1 < 2e-16
## medinc 51.959 1 5.7e-13
## manufact 62.359 1 2.9e-15
## GLOBAL 162.064 4 < 2e-16
## chisq df p
## pers 0.831 1 0.3619
## airport_dist 8.887 1 0.0029
## tourism 13.795 1 0.0002
## healthcare 39.805 1 2.8e-10
## popdens 3.149 1 0.0760
## GLOBAL 58.645 5 2.3e-11
## chisq df p
## pers 2.52e-01 1 0.61546
## age 4.88e-03 1 0.94429
## male 2.27e+01 1 1.9e-06
## conservative 7.45e+01 1 < 2e-16
## academics 1.14e+02 1 < 2e-16
## medinc 3.65e+01 1 1.6e-09
## manufact 4.77e+01 1 4.9e-12
## airport_dist 1.94e+00 1 0.16421
## tourism 1.26e+01 1 0.00038
## healthcare 2.73e+01 1 1.7e-07
## popdens 3.49e+01 1 3.6e-09
## GLOBAL 1.69e+02 11 < 2e-16
## chisq df p
## pers 0.402 1 0.53
## GLOBAL 0.402 1 0.53
## chisq df p
## pers 1.03 1 0.31
## age 1.37 1 0.24
## male 21.87 1 2.9e-06
## conservative 111.50 1 < 2e-16
## GLOBAL 134.61 4 < 2e-16
## chisq df p
## pers 0.603 1 0.44
## academics 153.090 1 < 2e-16
## medinc 54.952 1 1.2e-13
## manufact 68.508 1 < 2e-16
## GLOBAL 172.867 4 < 2e-16
## chisq df p
## pers 0.0332 1 0.8554
## airport_dist 8.7075 1 0.0032
## tourism 15.3470 1 8.9e-05
## healthcare 42.5992 1 6.7e-11
## popdens 3.5426 1 0.0598
## GLOBAL 61.8373 5 5.1e-12
## chisq df p
## pers 3.81e-03 1 0.95076
## age 1.66e-04 1 0.98973
## male 2.11e+01 1 4.4e-06
## conservative 7.67e+01 1 < 2e-16
## academics 1.19e+02 1 < 2e-16
## medinc 3.83e+01 1 6.1e-10
## manufact 5.16e+01 1 6.7e-13
## airport_dist 1.77e+00 1 0.18394
## tourism 1.36e+01 1 0.00023
## healthcare 3.01e+01 1 4.2e-08
## popdens 3.77e+01 1 8.1e-10
## GLOBAL 1.76e+02 11 < 2e-16
## chisq df p
## pers 49.7 1 1.8e-12
## GLOBAL 49.7 1 1.8e-12
## chisq df p
## pers 35.610 1 2.4e-09
## age 0.744 1 0.39
## male 21.775 1 3.1e-06
## conservative 109.483 1 < 2e-16
## GLOBAL 133.700 4 < 2e-16
## chisq df p
## pers 47.0 1 7.2e-12
## academics 158.3 1 < 2e-16
## medinc 58.3 1 2.2e-14
## manufact 62.3 1 2.9e-15
## GLOBAL 177.2 4 < 2e-16
## chisq df p
## pers 33.48 1 7.2e-09
## airport_dist 6.46 1 0.01102
## tourism 12.01 1 0.00053
## healthcare 41.31 1 1.3e-10
## popdens 3.34 1 0.06744
## GLOBAL 81.47 5 4.1e-16
## chisq df p
## pers 25.1056 1 5.4e-07
## age 0.0324 1 0.85712
## male 21.4504 1 3.6e-06
## conservative 71.5756 1 < 2e-16
## academics 119.2835 1 < 2e-16
## medinc 40.8184 1 1.7e-10
## manufact 47.3892 1 5.8e-12
## airport_dist 1.6077 1 0.20481
## tourism 11.5992 1 0.00066
## healthcare 28.6560 1 8.6e-08
## popdens 30.7441 1 2.9e-08
## GLOBAL 176.2965 11 < 2e-16
Assumptions GER COVID-19 growth rates
list_iterater(ger_list_results$ger_lm_prev_slope, test = 'qq')
list_iterater(ger_list_results$ger_lm_prev_slope, test = 'bp')
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.20014, df = 1, p-value = 0.6546
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 1.7565, df = 4, p-value = 0.7804
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 8.8926, df = 4, p-value = 0.06384
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 12.765, df = 5, p-value = 0.02568
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 19.536, df = 11, p-value = 0.05213
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.014025, df = 1, p-value = 0.9057
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 2.0882, df = 4, p-value = 0.7195
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 8.2989, df = 4, p-value = 0.08122
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 10.635, df = 5, p-value = 0.05912
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 18.827, df = 11, p-value = 0.06426
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.9348, df = 1, p-value = 0.3336
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 2.8075, df = 4, p-value = 0.5905
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 8.2707, df = 4, p-value = 0.08215
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 10.975, df = 5, p-value = 0.05188
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 18.978, df = 11, p-value = 0.06149
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 1.9621, df = 1, p-value = 0.1613
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 2.3503, df = 4, p-value = 0.6716
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 8.6682, df = 4, p-value = 0.06995
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 17.72, df = 5, p-value = 0.003318
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 19.885, df = 11, p-value = 0.04694
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.77295, df = 1, p-value = 0.3793
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 2.6459, df = 4, p-value = 0.6187
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 9.1115, df = 4, p-value = 0.05837
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 11.656, df = 5, p-value = 0.03981
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 19.53, df = 11, p-value = 0.05222
Assumptions US COVID-19 growth rates
list_iterater(us_list_results$us_lm_prev_slope, test = 'qq')
list_iterater(us_list_results$us_lm_prev_slope, test = 'bp')
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 25.881, df = 1, p-value = 3.63e-07
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 58.996, df = 4, p-value = 4.715e-12
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 27.503, df = 4, p-value = 1.573e-05
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 88.757, df = 5, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 121.41, df = 11, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.45532, df = 1, p-value = 0.4998
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 58.951, df = 4, p-value = 4.819e-12
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 35.857, df = 4, p-value = 3.096e-07
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 93.365, df = 5, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 127.65, df = 11, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 3.7171, df = 1, p-value = 0.05386
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 59.824, df = 4, p-value = 3.158e-12
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 21.132, df = 4, p-value = 0.0002982
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 84.501, df = 5, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 119.57, df = 11, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 7.0583, df = 1, p-value = 0.00789
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 65.59, df = 4, p-value = 1.933e-13
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 57.311, df = 4, p-value = 1.064e-11
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 108.23, df = 5, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 147.26, df = 11, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 12.544, df = 1, p-value = 0.0003975
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 58.66, df = 4, p-value = 5.545e-12
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 31.917, df = 4, p-value = 1.989e-06
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 94.189, df = 5, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 123.74, df = 11, p-value < 2.2e-16
Assumptions GER socdist onsets
list_iterater(ger_list_results$ger_cox_socdist_cpt, test = 'ph')
## chisq df p
## pers 0.187 1 0.67
## GLOBAL 0.187 1 0.67
## chisq df p
## pers 0.589 1 0.443
## age 1.510 1 0.219
## male 5.639 1 0.018
## conservative 1.333 1 0.248
## GLOBAL 8.287 4 0.082
## chisq df p
## pers 0.07128 1 0.789
## academics 0.00457 1 0.946
## medinc 6.30499 1 0.012
## manufact 5.40654 1 0.020
## GLOBAL 9.07479 4 0.059
## chisq df p
## pers 0.0093 1 0.923
## airport_dist 3.7655 1 0.052
## tourism 0.4039 1 0.525
## healthcare 0.9540 1 0.329
## popdens 0.0948 1 0.758
## GLOBAL 5.0000 5 0.416
## chisq df p
## pers 0.0439 1 0.834
## age 3.8044 1 0.051
## male 6.2038 1 0.013
## conservative 2.3823 1 0.123
## academics 0.0481 1 0.826
## medinc 3.3040 1 0.069
## manufact 2.3737 1 0.123
## airport_dist 6.1123 1 0.013
## tourism 1.5087 1 0.219
## healthcare 1.5767 1 0.209
## popdens 0.0263 1 0.871
## onset_prev 2.8203 1 0.093
## slope_prev 4.1380 1 0.042
## GLOBAL 17.3136 13 0.185
## chisq df p
## pers 0.284 1 0.59
## GLOBAL 0.284 1 0.59
## chisq df p
## pers 1.15 1 0.284
## age 1.34 1 0.247
## male 5.15 1 0.023
## conservative 1.12 1 0.290
## GLOBAL 7.65 4 0.105
## chisq df p
## pers 0.5249 1 0.469
## academics 0.0037 1 0.952
## medinc 5.4471 1 0.020
## manufact 5.4390 1 0.020
## GLOBAL 7.8952 4 0.095
## chisq df p
## pers 0.1079 1 0.743
## airport_dist 4.6196 1 0.032
## tourism 0.4192 1 0.517
## healthcare 1.2591 1 0.262
## popdens 0.0509 1 0.822
## GLOBAL 6.7462 5 0.240
## chisq df p
## pers 0.5092 1 0.4755
## age 3.8442 1 0.0499
## male 5.7297 1 0.0167
## conservative 2.3451 1 0.1257
## academics 0.0958 1 0.7570
## medinc 3.2215 1 0.0727
## manufact 2.2607 1 0.1327
## airport_dist 6.8590 1 0.0088
## tourism 1.4968 1 0.2212
## healthcare 1.6372 1 0.2007
## popdens 0.0114 1 0.9150
## onset_prev 3.0881 1 0.0789
## slope_prev 4.5326 1 0.0333
## GLOBAL 17.6092 13 0.1729
## chisq df p
## pers 1.04 1 0.31
## GLOBAL 1.04 1 0.31
## chisq df p
## pers 1.52 1 0.218
## age 1.18 1 0.276
## male 4.82 1 0.028
## conservative 1.05 1 0.307
## GLOBAL 7.57 4 0.109
## chisq df p
## pers 0.9332 1 0.334
## academics 0.0254 1 0.873
## medinc 5.5409 1 0.019
## manufact 4.7740 1 0.029
## GLOBAL 8.6942 4 0.069
## chisq df p
## pers 1.232 1 0.27
## airport_dist 4.230 1 0.04
## tourism 0.475 1 0.49
## healthcare 1.037 1 0.31
## popdens 0.103 1 0.75
## GLOBAL 5.784 5 0.33
## chisq df p
## pers 0.8818 1 0.3477
## age 3.7021 1 0.0543
## male 5.4792 1 0.0192
## conservative 2.2946 1 0.1298
## academics 0.1120 1 0.7379
## medinc 2.5620 1 0.1095
## manufact 1.7891 1 0.1810
## airport_dist 7.1031 1 0.0077
## tourism 1.7503 1 0.1858
## healthcare 1.6702 1 0.1962
## popdens 0.0123 1 0.9115
## onset_prev 2.9767 1 0.0845
## slope_prev 4.3407 1 0.0372
## GLOBAL 17.9668 13 0.1588
## chisq df p
## pers 0.667 1 0.41
## GLOBAL 0.667 1 0.41
## chisq df p
## pers 1.50 1 0.221
## age 1.30 1 0.255
## male 4.96 1 0.026
## conservative 1.09 1 0.295
## GLOBAL 8.17 4 0.086
## chisq df p
## pers 0.96909 1 0.325
## academics 0.00695 1 0.934
## medinc 5.51116 1 0.019
## manufact 5.08746 1 0.024
## GLOBAL 7.82380 4 0.098
## chisq df p
## pers 0.4477 1 0.503
## airport_dist 4.4029 1 0.036
## tourism 0.4347 1 0.510
## healthcare 1.0666 1 0.302
## popdens 0.0851 1 0.770
## GLOBAL 5.8358 5 0.323
## chisq df p
## pers 1.3945 1 0.2377
## age 3.7995 1 0.0513
## male 5.6669 1 0.0173
## conservative 2.3341 1 0.1266
## academics 0.0935 1 0.7598
## medinc 3.2418 1 0.0718
## manufact 2.2310 1 0.1353
## airport_dist 6.8871 1 0.0087
## tourism 1.5300 1 0.2161
## healthcare 1.6230 1 0.2027
## popdens 0.0108 1 0.9173
## onset_prev 2.9882 1 0.0839
## slope_prev 4.5077 1 0.0337
## GLOBAL 17.5141 13 0.1769
## chisq df p
## pers 0.66 1 0.42
## GLOBAL 0.66 1 0.42
## chisq df p
## pers 0.268 1 0.605
## age 1.388 1 0.239
## male 4.860 1 0.027
## conservative 1.229 1 0.268
## GLOBAL 6.221 4 0.183
## chisq df p
## pers 0.91068 1 0.340
## academics 0.00965 1 0.922
## medinc 5.97668 1 0.014
## manufact 5.24467 1 0.022
## GLOBAL 7.79339 4 0.099
## chisq df p
## pers 0.6571 1 0.418
## airport_dist 4.0898 1 0.043
## tourism 0.4325 1 0.511
## healthcare 1.0549 1 0.304
## popdens 0.0785 1 0.779
## GLOBAL 5.3354 5 0.376
## chisq df p
## pers 0.5507 1 0.4580
## age 3.8479 1 0.0498
## male 5.6687 1 0.0173
## conservative 2.3884 1 0.1222
## academics 0.0979 1 0.7544
## medinc 3.2705 1 0.0705
## manufact 2.2318 1 0.1352
## airport_dist 6.7326 1 0.0095
## tourism 1.5199 1 0.2176
## healthcare 1.5803 1 0.2087
## popdens 0.0102 1 0.9194
## onset_prev 3.0841 1 0.0791
## slope_prev 4.5356 1 0.0332
## GLOBAL 18.3991 13 0.1429
Assumptions US socdist onsets
list_iterater(us_list_results$us_cox_socdist_cpt, test = 'ph')
## chisq df p
## pers 40.1 1 2.4e-10
## GLOBAL 40.1 1 2.4e-10
## chisq df p
## pers 40.37 1 2.1e-10
## age 3.15 1 0.07575
## male 8.88 1 0.00288
## conservative 12.21 1 0.00047
## GLOBAL 47.17 4 1.4e-09
## chisq df p
## pers 35.276 1 2.9e-09
## academics 10.776 1 0.001
## medinc 0.712 1 0.399
## manufact 6.477 1 0.011
## GLOBAL 37.669 4 1.3e-07
## chisq df p
## pers 45.88 1 1.3e-11
## airport_dist 33.83 1 6.0e-09
## tourism 25.00 1 5.7e-07
## healthcare 1.81 1 0.18
## popdens 47.05 1 6.9e-12
## GLOBAL 101.93 5 < 2e-16
## chisq df p
## pers 34.785 1 3.7e-09
## age 1.678 1 0.1953
## male 6.300 1 0.0121
## conservative 15.885 1 6.7e-05
## academics 10.398 1 0.0013
## medinc 0.941 1 0.3321
## manufact 7.793 1 0.0052
## airport_dist 29.969 1 4.4e-08
## tourism 19.239 1 1.2e-05
## healthcare 0.718 1 0.3968
## popdens 34.662 1 3.9e-09
## onset_prev 52.981 1 3.4e-13
## slope_prev 59.230 1 1.4e-14
## GLOBAL 121.454 13 < 2e-16
## chisq df p
## pers 12.4 1 0.00043
## GLOBAL 12.4 1 0.00043
## chisq df p
## pers 12.03 1 0.00052
## age 2.36 1 0.12437
## male 6.84 1 0.00890
## conservative 10.00 1 0.00157
## GLOBAL 26.40 4 2.6e-05
## chisq df p
## pers 10.018 1 0.0016
## academics 8.193 1 0.0042
## medinc 0.203 1 0.6524
## manufact 5.864 1 0.0155
## GLOBAL 26.466 4 2.5e-05
## chisq df p
## pers 8.91 1 0.0028
## airport_dist 35.75 1 2.2e-09
## tourism 24.41 1 7.8e-07
## healthcare 1.13 1 0.2872
## popdens 63.76 1 1.4e-15
## GLOBAL 112.08 5 < 2e-16
## chisq df p
## pers 8.922 1 0.00282
## age 1.096 1 0.29514
## male 5.384 1 0.02032
## conservative 14.733 1 0.00012
## academics 8.988 1 0.00272
## medinc 0.515 1 0.47281
## manufact 8.175 1 0.00425
## airport_dist 32.147 1 1.4e-08
## tourism 18.978 1 1.3e-05
## healthcare 0.414 1 0.51984
## popdens 36.422 1 1.6e-09
## onset_prev 49.691 1 1.8e-12
## slope_prev 59.684 1 1.1e-14
## GLOBAL 120.284 13 < 2e-16
## chisq df p
## pers 0.561 1 0.45
## GLOBAL 0.561 1 0.45
## chisq df p
## pers 0.427 1 0.51355
## age 2.983 1 0.08417
## male 7.640 1 0.00571
## conservative 10.147 1 0.00145
## GLOBAL 18.922 4 0.00081
## chisq df p
## pers 0.0957 1 0.7570
## academics 9.7760 1 0.0018
## medinc 0.5531 1 0.4571
## manufact 6.6345 1 0.0100
## GLOBAL 17.1795 4 0.0018
## chisq df p
## pers 0.402 1 0.53
## airport_dist 32.089 1 1.5e-08
## tourism 26.039 1 3.3e-07
## healthcare 1.454 1 0.23
## popdens 59.669 1 1.1e-14
## GLOBAL 102.636 5 < 2e-16
## chisq df p
## pers 1.28e-03 1 0.97144
## age 1.39e+00 1 0.23869
## male 5.95e+00 1 0.01473
## conservative 1.48e+01 1 0.00012
## academics 1.03e+01 1 0.00133
## medinc 8.99e-01 1 0.34313
## manufact 8.21e+00 1 0.00417
## airport_dist 2.96e+01 1 5.2e-08
## tourism 1.96e+01 1 9.6e-06
## healthcare 5.16e-01 1 0.47244
## popdens 3.61e+01 1 1.9e-09
## onset_prev 5.21e+01 1 5.2e-13
## slope_prev 6.00e+01 1 9.3e-15
## GLOBAL 1.18e+02 13 < 2e-16
## chisq df p
## pers 16.4 1 5.1e-05
## GLOBAL 16.4 1 5.1e-05
## chisq df p
## pers 15.94 1 6.5e-05
## age 2.83 1 0.0925
## male 7.42 1 0.0065
## conservative 10.10 1 0.0015
## GLOBAL 28.85 4 8.4e-06
## chisq df p
## pers 13.905 1 0.00019
## academics 9.124 1 0.00252
## medinc 0.395 1 0.52976
## manufact 6.149 1 0.01315
## GLOBAL 32.382 4 1.6e-06
## chisq df p
## pers 11.5 1 0.0007
## airport_dist 33.8 1 6.0e-09
## tourism 24.3 1 8.3e-07
## healthcare 1.1 1 0.2943
## popdens 61.5 1 4.4e-15
## GLOBAL 116.8 5 < 2e-16
## chisq df p
## pers 11.805 1 0.00059
## age 1.281 1 0.25765
## male 5.737 1 0.01661
## conservative 14.695 1 0.00013
## academics 9.849 1 0.00170
## medinc 0.717 1 0.39707
## manufact 8.389 1 0.00378
## airport_dist 30.178 1 3.9e-08
## tourism 18.997 1 1.3e-05
## healthcare 0.393 1 0.53092
## popdens 35.993 1 2.0e-09
## onset_prev 51.526 1 7.1e-13
## slope_prev 60.946 1 5.9e-15
## GLOBAL 122.473 13 < 2e-16
## chisq df p
## pers 8.41 1 0.0037
## GLOBAL 8.41 1 0.0037
## chisq df p
## pers 8.65 1 0.00328
## age 1.99 1 0.15812
## male 7.00 1 0.00814
## conservative 10.05 1 0.00152
## GLOBAL 19.68 4 0.00058
## chisq df p
## pers 9.659 1 0.00188
## academics 7.704 1 0.00551
## medinc 0.124 1 0.72482
## manufact 7.032 1 0.00801
## GLOBAL 20.333 4 0.00043
## chisq df p
## pers 7.99 1 0.0047
## airport_dist 34.83 1 3.6e-09
## tourism 24.38 1 7.9e-07
## healthcare 0.57 1 0.4502
## popdens 51.22 1 8.2e-13
## GLOBAL 97.26 5 < 2e-16
## chisq df p
## pers 9.841 1 0.0017
## age 0.987 1 0.3204
## male 5.645 1 0.0175
## conservative 15.669 1 7.5e-05
## academics 9.134 1 0.0025
## medinc 0.439 1 0.5078
## manufact 9.371 1 0.0022
## airport_dist 30.646 1 3.1e-08
## tourism 19.652 1 9.3e-06
## healthcare 0.180 1 0.6712
## popdens 35.728 1 2.3e-09
## onset_prev 50.955 1 9.4e-13
## slope_prev 60.718 1 6.6e-15
## GLOBAL 122.278 13 < 2e-16
Assumptions GER socdist adjustment levels
list_iterater(ger_list_results$ger_lm_socdist_mean, test = 'qq')
list_iterater(ger_list_results$ger_lm_socdist_mean, test = 'bp')
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 6.6073, df = 1, p-value = 0.01016
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 11.402, df = 4, p-value = 0.02239
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 38.391, df = 4, p-value = 9.306e-08
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.91771, df = 5, p-value = 0.9689
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 36.57, df = 13, p-value = 0.0004836
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 2.8103, df = 1, p-value = 0.09366
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 16.321, df = 4, p-value = 0.002617
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 43.588, df = 4, p-value = 7.812e-09
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 3.9413, df = 5, p-value = 0.5579
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 40.982, df = 13, p-value = 9.594e-05
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 3.9066, df = 1, p-value = 0.0481
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 14.173, df = 4, p-value = 0.006764
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 37.668, df = 4, p-value = 1.312e-07
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 1.6777, df = 5, p-value = 0.8917
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 36.106, df = 13, p-value = 0.0005712
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 6.5055, df = 1, p-value = 0.01075
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 15.626, df = 4, p-value = 0.003565
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 40.792, df = 4, p-value = 2.968e-08
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 1.6497, df = 5, p-value = 0.8952
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 39.059, df = 13, p-value = 0.0001956
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 2.3883, df = 1, p-value = 0.1222
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 14.994, df = 4, p-value = 0.004713
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 38.685, df = 4, p-value = 8.091e-08
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 2.6498, df = 5, p-value = 0.7538
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 38.358, df = 13, p-value = 0.000253
Assumptions US socdist adjustment levels
list_iterater(us_list_results$us_lm_socdist_mean, test = 'qq')
list_iterater(us_list_results$us_lm_socdist_mean, test = 'bp')
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 11.773, df = 1, p-value = 0.0006008
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 11.552, df = 4, p-value = 0.02101
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 9.536, df = 4, p-value = 0.04901
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 37.618, df = 5, p-value = 4.501e-07
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 33.974, df = 13, p-value = 0.001215
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 6.7876, df = 1, p-value = 0.00918
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 10.068, df = 4, p-value = 0.03929
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 11.336, df = 4, p-value = 0.02303
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 49.661, df = 5, p-value = 1.626e-09
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 34.866, df = 13, p-value = 0.0008878
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.18699, df = 1, p-value = 0.6654
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 14.373, df = 4, p-value = 0.006195
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 10.332, df = 4, p-value = 0.03519
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 50.528, df = 5, p-value = 1.081e-09
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 34.641, df = 13, p-value = 0.0009611
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 14.662, df = 1, p-value = 0.0001286
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 12.049, df = 4, p-value = 0.01699
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 15.089, df = 4, p-value = 0.004519
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 54.746, df = 5, p-value = 1.472e-10
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 36.335, df = 13, p-value = 0.0005262
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 9.3516, df = 1, p-value = 0.002228
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 20.051, df = 4, p-value = 0.000488
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 11.455, df = 4, p-value = 0.0219
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 51.643, df = 5, p-value = 6.386e-10
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 36.846, df = 13, p-value = 0.000438